Document warehousing extends data warehousing to content mining and retrieval. Document warehousing attempts to extract semantic information from collections of unstructured documents to provide conceptual information with a high degree of precision and recall. Documents in a document warehouse share several properties. First, the documents lack a common structure or shared type. Second, semantically-related documents are integrated through text mining. Third, essential document features are extracted and explicitly stored as part of the document warehouse. Finally, documents are often retrieved from multiple and disparate sources, such as over the Internet or as electronic messages.
Document warehouses are built in stages to deal with a wide range of information sources. First, document sources are identified and documents are retrieved into a repository. For example, the document sources could be electronic messaging folders or Web content retrieved over the Internet. Once retrieved, the documents are pre-processed to format and regularize the information into a consistent manner. Next, during text analysis, text mining is performed to extract semantic content, including identifying dominant themes, extracting key features and summarizing the content. Finally, metadata is compiled from the semantic context to explicate essential attributes. Preferably, the metadata is provided in a format amenable to normalized queries, such as database management tools. Document warehousing is described in D. Sullivan, “Document Warehousing and Text Mining, Techniques for Improving Business Operations, Marketing, and Sales,” Chs. 1–3, Wiley Computer Publishing (2001), the disclosure of which is incorporated by reference.
Text mining is at the core of the data warehousing process. Text mining involves the compiling, organizing and analyzing of document collections to support the delivery of targeted types of information and to discover relationships between relevant facts. However, identifying relevant content can be difficult. First, extracting relevant content requires a high degree of precision and recall. Precision is the measure of how well the documents returned in response to a query actually address the query criteria. Recall is the measure of what should have been returned by the query. Typically, the broader and less structured the documents, the lower the degree of precision and recall. Second, analyzing an unstructured document collection without the benefit of a priori knowledge in the form of keywords and indices can present a potentially intractable problem space. Finally, synonymy and polysemy can cloud and confuse extracted content. Synonymy refers to multiple words having the same meaning and polysemy refers to a single word with multiple meanings. Fine-grained text mining must reconcile synonymy and polysemy to yield meaningful results.
In the prior art, text mining is performed in two ways. First, syntactic searching provides a brute force approach to analyzing and extracting content based on literal textual attributes found in each document. Syntactic searching includes keyword and proximate keyword searching as well as rule-based searching through Boolean relationships. Syntactic searching relies on predefined indices of keywords and stop words to locate relevant information. However, there are several ways to express any given concept. Accordingly, syntactic searching can fail to yield satisfactory results due to incomplete indices and poorly structured search criteria.
A more advanced prior art approach uses a vector space model to search for underlying meanings in a document collection. The vector space model employs a geometric representation of documents using word vectors. Individual keywords are mapped into vectors in multi-dimensional space along axes representative of query search terms. Significant terms are assigned a relative weight and semantic content is extracted based on threshold filters. Although substantially overcoming the shortcomings of syntactic searching, the multivariant and multidimensional nature of the vector space model can lead to a computationally intractable problem space. As well, the vector space model fails to resolve the problems of synonymy and polysemy.
Therefore, there is a need for an approach to dynamically evaluating concepts inherent in a collection of documents. Such an approach would preferably dynamically discover the latent meanings without the use of a priori knowledge or indices. Rather, the approach would discover semantic relationships between individual terms given the presence of another item.
There is a further need for an approach to providing a graphical visualization of concepts extracted from a document set through semantic indexing. Preferably, such an approach would extract the underlying meanings of documents through statistics and linear algebraic techniques to find clusters of terms and phrases representative of the concepts.